Takato Fujimoto — accepted AI-conference papers

2022

Autoregressive Variational Autoencoder with a Hidden Semi-Markov Model-Based Structured Attention for Speech Synthesis

Takato Fujimoto, Kei Hashimoto, Yoshihiko Nankaku, Keiichi Tokuda

ICASSP 2022accepted

This paper proposes an autoregressive speech synthesis model based on the variational autoencoder incorporating latent sequence representation for acoustic and linguistic features and the structure of a hidden semi-Markov model (HSMM). Although autoregressive models can provide efficient and accurat…

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2020

Semi-Supervised Learning Based on Hierarchical Generative Models for End-to-End Speech Synthesis

Takato Fujimoto, Shinji Takaki, Kei Hashimoto, Keiichiro Oura, Yoshihiko Nankaku, Keiichi Tokuda

ICASSP 2020accepted

This paper proposes a general framework of semi-supervised learning based on hierarchical generative models and adapts it to a Japanese end-to-end text-to-speech (TTS) system. In English TTS, several end-to-end systems have recently achieved sound quality close to that of natural human speech. Howev…

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